class_docstr: A type-safe wrapper for a list of quantile forecasts, stored as NumPy
  arrays.
class_docstr_tok: ["A","type-safe","wrapper","for","a","list","of","quantile","forecasts",",","stored","as","NumPy","arrays","."]
docstr: Returns the median forecasts for all time series.
  
  NumPy array of shape [N, T] (N: number of forecasts, T: forecast
  horizon).
docstr_tok: ["Returns","the","median","forecasts","for","all","time","series",".","NumPy","array","of","shape","[","N",",","T","]","(","N",":","number","of","forecasts",",","T",":","forecast","horizon",")","."]
code: def median(self) -> npt.NDArray[np.float32]:
      """
      Returns the median forecasts for all time series.
  
      NumPy array of shape [N, T] (N: number of forecasts, T: forecast
      horizon).
      """
      i = self.quantiles.index("0.5")
      return self.values[:, i]
  
file_path: gluonts/src/gluonts/nursery/tsbench/src/tsbench/forecasts/quantile.py
imports: from __future__ import annotations
  from dataclasses import dataclass
  from pathlib import Path
  from typing import List
  import numpy
  import numpy.typing
  import pandas
  from gluonts.model.forecast import QuantileForecast
  from gluonts.time_feature import get_seasonality
label_desc: Use image node_id 4 for calling the QuantileForecasts obj's underlying member method code with example usage: obj.median() and returns: self